The study investigates classifying Fitzpatrick skin tones from wearable photoplethysmography (PPG) signals, noting that traditional accuracy is low (40‑55 %) due to subjective labeling. By introducing a fuzzy accuracy metric—treating predictions within one class of the label as correct—accuracy rises dramatically, reaching up to 96 % on tree‑based models and 85‑87 % on deep learning and feature‑based approaches. The results suggest that PPG signals contain discernible skin‑tone information, especially when evaluated with the fuzzy metric.
arXiv:2606. 15927v1 Announce Type: new Abstract: Diabetes and extreme blood sugar levels are some of the major health problems faced by humans today across the world.
arXiv:2607. 19999v1 Announce Type: new Abstract: This Good Practice Guide presents work done in the QUMPHY project (Uncertainty quantification for machine learning models applied to photoplethysmography signals) that considered both machine learning and uncertainty quantification for problems which used photoplethysmography (PPG) signals from wearable devices as input.
The study examines why dermatology AI models, largely trained on light‑skinned, cancer‑focused images, perform poorly when applied to diverse patient populations. By comparing a cancer‑trained baseline, two dermatology foundation models, and a general‑purpose vision model on tone‑stratified and disease‑shifted datasets, the authors find that disease‑distribution shift, rather than skin‑tone underrepresentation, is the primary cause of generalization failure. Representation analysis shows that cancer‑specialized features lack transferable structure, while dermatology‑pretrained features maintain stronger clustering, and lightweight adaptation with about ten labeled examples per category can recover most performance.
The Entropy Triangle Method (ETM) is a new framework designed to predict cardiac arrhythmias. It combines feature engineering, entropy triangle oversampling, and disease prediction, and was tested on a 12‑lead ECG database of 10,646 patients with 11 heart rhythm categories. The study reports that the method achieves over 85% accuracy for non‑sinus rhythms, with support vector classifiers and a novel oversampling technique termed "shark scent" yielding the best performance.
arXiv:2607. 06598v1 Announce Type: cross Abstract: Heart rate measurement is one of the key requirements for real-time health monitoring, in particular for health caring of elderly people.
arXiv:2509. 11606v4 Announce Type: replace-cross Abstract: Cardiovascular diseases (CVDs) are the leading cause of death worldwide, accounting for approximately 17.
arXiv:2609.36400v1 Announce Type: cross Abstract: Deep learning classifiers for dermoscopic skin lesions often reach high in-distribution accuracy while quietly relying on spurious background cues su...
arXiv:2608. 08521v1 Announce Type: cross Abstract: Face recognition systems face two distinct, commonly-separated failure modes: spoofing, where an impostor presents a photograph or video of an authorized user, and disguise, where a legitimate user is rejected because their appearance differs from their enrolled template due to accessories, facial hair, illumination, or pose.
Gaussian head avatars typically model intrinsic facial appearance as temporally static, omitting subtle cardiac-induced skin-color variation. We propose $\unicode{x1F493}$Heartian, a physiology-aware...
arXiv:2607. 26170v1 Announce Type: cross Abstract: This study developed a hybrid computer vision method to quantify exposed skin from images for dermal exposure assessment.
arXiv:2606. 11505v1 Announce Type: cross Abstract: Biometric systems are increasingly deployed in security applications; however, they remain vulnerable to spoofing attacks, in which attackers exploit counterfeit biometric data to gain unauthorized access.
arXiv:2608. 12117v1 Announce Type: new Abstract: Arterial pulse waveform morphology evolves with age, reflecting structural and functional changes in the cardiovascular system.